These cultural icons share a tragic connection: They all died too young from accidental overdoses of prescription painkillers – mostly synthetic opioids – or a cocktail of prescription drugs.

Download the white paper: Data and Analytics to Combat the Opioid Epidemic

The opioid epidemic presents a much broader data management challenge than the traditional PDMP. More and better analytics and nontraditional ways of working together across agencies and information systems are needed to inform better decisions and outcomes.

Headline-making celebrity deaths only hint at the scope of the problem. According to the US Centers for Disease Control and Prevention (CDC), drug overdose is the leading cause of accidental death in the US, with 47,055 lethal drug overdoses in 2014. Opioid addiction is driving this epidemic, with 18,893 overdose deaths related to prescription pain relievers.

The brands are household names: Percocet, Vicodin, OxyContin. Their medical origins lead to the perception that they are safer than street drugs, but their potency makes them dangerous. Consider that by 2012, fentanyl (Duragesic, Actiq, Fentora, etc.) had become the most widely used synthetic opioid for palliative pain relief. Fentanyl is about 80 to 100 times stronger than morphine and 40 to 50 times stronger than pharmaceutical-grade, 100-percent-pure heroin. Accidental contact with even a used fentanyl patch can kill a child.

The euphoria these drugs can produce makes them highly addictive and widely misused. The CDC has officially declared it an epidemic. It affects people of all backgrounds and geographies across the socioeconomic spectrum.

To tackle this epidemic, a lot of effort has been put into state-run prescription drug monitoring programs (PDMPs) that establish databases of prescriptions written and filled, with secure online portals for accessing that data. The concept is good: Provide a comprehensive view of prescription and use patterns to make it easy for pharmacies, providers and policymakers to spot telltale signs of abuse/addiction. But in practice, PDMPs have some shortcomings:

Data must be integrated from many sources. Combining data from numerous pharmacies and medical providers to get a single view of patient behavior has been complex and time-consuming, a challenge compounded by privacy issues.

That data isn’t necessarily turned into insight. Most PDMPs simply collect data about what was prescribed and what was dispensed. Few actively analyze the data to find inappropriate or suspicious behaviors.

Many physicians don’t use them. PDMP systems are mandatory in 49 states, but only 22 require prescribing physicians to consult them before prescribing, and compliance is far from perfect. Physicians complain that it doesn’t fit their workflow and that access controls make it too time-consuming. With the web portal already open, it can still take three to seven minutes to look up a patient and evaluate the records. If the physician has only 15 minutes with the patient, that’s too long.

Compounding the promise and potential of PDMPs

In the International Institute for Analytics report Data and Analytics to Combat the Opioid Epidemic, Steve Kearney and Jen Dunham of SAS take an in-depth view of the issue and how to address it. Their key message: It’s not enough to say, “Let’s improve the current PDMPs so providers will use them.” We need to have more and better knowledge out of these programs.

When a patient is seeing three specialists and using more than one pharmacy, the PDMP’s simple list of prescriptions can get difficult to decipher, and the prescription is just one piece of the puzzle. You need to be able to bring in other data, such as electronic health records and emergency room records, to get full context.

Better data can do much more for PDMPs than just identify “pill mills” for investigation. Better data can inform better treatment protocols, better provider education and better policy decisions. With more comprehensive data and the analytics to make sense of it, everybody benefits:

Physicians can understand how their treatments and results compare with those of their peers, as well as what specific patterns give early warning of addiction or overdose. They can recognize patient scenarios that analytics has uncovered, prescribe or refer correctly, and achieve the best overall outcomes.

Payers can determine whether prescriptions are appropriate or potentially being misused or diverted, and take action to protect their patients from addiction while avoiding paying out for fraud or misuse.

Pharmacies can understand how their dispensing activity compares by geography, payment source, provider and patient mix. Data and analytics can identify anomalies that might signal areas of concern.

States can better understand where to appropriate funding for treatment facilities based on the combination of local need and which facilities have the best outcomes for patients with specific histories.

Health care providers and researchers can develop better treatment protocols, both for pain in the first place and for remediation when patients are becoming dependent on the drugs.

Bringing the data together helps the various constituencies work together. It helps everybody see and understand the bigger picture, as well as clarify and focus on the outcomes we want – saving lives instead of making headlines.